Unequal weight planar motion vector derivation

ABSTRACT

A system and method of planar motion vector derivation which, in some embodiments can employ an unequal weighted combination of adjacent motion vectors. In some embodiments, motion vector information associated with a bottom right pixel or block adjacent to a current coding unit can be derived from motion information associated with a top row or top neighboring row of a current coding unit and motion information associated with a left column or left neighboring column of a current coding unit. Weighted or non-weighted combinations of such values can be combined in a planar mode prediction model to derive associated motion information for bottom and/or right adjacent pixels or blocks.

CLAIM OF PRIORITY

This Application is a continuation of U.S. patent application Ser. No.17/097,312 filed Nov. 13, 2020, which is a continuation of U.S. patentapplication Ser. No. 16/384,763 filed Apr. 15, 2019, now U.S. Pat. No.10,841,575, which claims priority under 35 U.S.C. § 119(e) from earlierfiled U.S. Provisional Application Ser. No. 62/657,831, filed Apr. 15,2018, the contents of each of which are incorporated herein by referencein their entirety.

TECHNICAL FIELD

The present disclosure relates to the field of video coding,particularly coding efficiency increases and memory burden associatedwith reduction of the number of stored collocated pictures.

BACKGROUND

The technical improvements in evolving video coding standards illustratethe trend of increasing coding efficiency to enable higher bit-rates,higher resolutions, and better video quality. The Joint VideoExploration Team developed a new video coding scheme referred to as JVETand is developing a newer video coding scheme referred to a VersatileVideo Coding (VVC)—the complete contents of the VVC 7^(th) edition ofdraft 2 of the standard titled Versatile Video Coding (Draft 2) by JVETpublished Oct. 1, 2018 is hereby incorporated herein by reference.Similar to other video coding schemes like HEVC (High Efficiency VideoCoding), both JVET and VVC are block-based hybrid spatial and temporalpredictive coding schemes. However, relative to HEVC, JVET and VVCinclude many modifications to bitstream structure, syntax, constraints,and mapping for the generation of decoded pictures. JVET has beenimplemented in Joint Exploration Model (JEM) encoders and decoders, butVVC is not anticipated to be implemented until early 2020.

Current and anticipated video coding schemes typically utilize simpleassumptions adjacent pixel/block similarity to determine values ofpredicted intensity values for neighboring pixels or pixel blocks. Thesame process can be implemented for associated Motion Vectors (MV).However, such an assumption can lead to erroneous result. What is neededis a system and method of unequal weight planar motion vectorderivation.

SUMMARY

A system of one or more computers can be configured to performparticular operations or actions by virtue of having software, firmware,hardware, or a combination of them installed on the system that inoperation causes or cause the system to perform the actions. One or morecomputer programs can be configured to perform particular operations oractions by virtue of including instructions that, when executed by dataprocessing apparatus, cause the apparatus to perform the actions. Onegeneral aspect comprises: identifying a coding unit having a topneighboring row, a left, neighboring column, a bottom neighboring rowand a right neighboring column; determining motion informationassociated with a bottom right neighboring pixel positioned at theintersection of said bottom neighboring row and said right neighboringcolumn based at least in part on motion information associated with saidtop row and said left neighboring column; determining motion informationassociated with said right neighboring column, based at least in partupon said motion information associated with said bottom rightneighboring pixel; and encoding said coding unit. Other embodiments ofthis aspect can comprise corresponding computer systems, apparatus, andcomputer programs recorded on one or more computer storage devices, eachconfigured to perform the actions of the methods.

Implementations can comprise one or more of the following features:determining motion information associated with said bottom neighboringrow, based at least in part upon said motion information associated withsaid bottom right neighboring pixel; where a planar coding mode isemployed; determining a first weight value associated with said topneighboring row, and determining a second weight value associated withsaid left neighboring column, where said step of determining motioninformation associated with said bottom right neighboring pixel is basedat least in part on a combination of said first weight value and saidmotion information associated with said top neighboring row with acombination of said second weight value with said motion informationassociated with said left neighboring column. Implementations of thedescribed techniques can further comprise hardware, a method or process,or computer software on a computer-accessible medium.

Additionally, general aspects can comprise a system of video codingincluding: storing in memory a coding unit having a top neighboring row,a left neighboring column, a bottom neighboring row and a rightneighboring column; determining and storing in said memory motioninformation associated with a bottom right neighboring pixel positionedat the intersection of said bottom neighboring row and said rightneighboring column based at least in part on motion informationassociated with said top row and said left neighboring column;determining and storing in memory motion information associated withsaid right neighboring column, based at least in part upon said motioninformation associated with said bottom right neighboring pixel; andencoding said coding unit. Other embodiments of this aspect includecorresponding computer systems, apparatus, and computer programsrecorded on one or more computer storage devices, each configured toperform the actions of the methods.

Implementations can comprise one or more of the following features: Thesystem of video coding further including: determining and storing inmemory motion information associated with said bottom neighboring row,based at least in part upon said motion information associated with saidbottom right neighboring pixel. The system of video coding furtherincluding: determining and storing in memory a first weight valueassociated with said top neighboring row, and determining and storgingin memory a second weight value associated with said left neighboringcolumn, where said step of determining motion information associatedwith said bottom right neighboring pixel is based at least in part on acombination of said first weight value and said motion informationassociated with said top neighboring row with a combination of saidsecond weight value with said motion information associated with saidleft neighboring column. Implementations of the described techniques mayinclude hardware a method or process, or computer software on acomputer-accessible medium.

BRIEF DESCRIPTION OF THE DRAWINGS

Further details of the present invention are explained with the help ofthe attached drawings in which:

FIG. 1 depicts division of a frame into a plurality of Coding Tree Units(CTUs),

FIG. 2a-2c depict exemplary partitioning of a CTU into Coding Units(CUs).

FIG. 3 depicts a quadtree plus binary tree (QTBT) representation of FIG.2's CU partitioning.

FIG. 4 depicts a simplified block diagram for CU coding in a JVET or VVCencoder.

FIG. 5 depicts possible intra prediction modes for luma components inJVET of VVC.

FIG. 6 depicts a simplified block diagram for CU coding in a JVET of VVCdecoder.

FIGS. 7a-7b depict graphical representations of horizontal and verticalprediction calculators, respectively.

FIG. 8 depicts an exemplary embodiment of a weight parameter, S[n], inwhich sum of the width and height is 256.

FIG. 9 depicts an exemplary embodiment of a weight parameter, S[n], inwhich sum of the width and height is 512.

FIG. 10 depicts a block flow diagram of a system and method of unequalweight planar motion vector derivation.

FIG. 11 depicts an embodiment of a computer system adapted andconfigured to provide for variable template size for template matching.

FIG. 12 depicts an embodiment of video encoder/decoder adapted andconfigured to provide for variable template size for template matching.

DETAILED DESCRIPTION

FIG. 1 depicts division of a frame into a plurality of Coding Tree Units(CTUs) 100. A frame can be an image in a video sequence. A frame caninclude a matrix, or set of matrices, with pixel values representingintensity measures in the image. Thus, a set of these matrices cangenerate a video sequence. Pixel values can be defined to representcolor and brightness in full color video coding, where pixels aredivided into three channels. For example, in a YCbCr color space pixelscan have a luma value, Y, that represents gray level intensity in theimage, and two chrominance values, Cb and Cr, that represent the extentto which color differs from gray to blue and red. In other embodiments,pixel values can be represented with values in different color spaces ormodels. The resolution of the video can determine the number of pixelsin a frame. A higher resolution can mean more pixels and a betterdefinition of the image, but can also lead to higher bandwidth, storage,and transmission requirements.

Frames of a video sequence can be encoded and decoded using JVET. JVETis a video coding scheme being developed by the Joint Video ExplorationTeam. Versions of JVET have been implemented in JEM (Joint ExplorationModel) encoders and decoders. Similar to other video coding schemes likeHEVC (High Efficiency Video Coding), JVET is a block-based hybridspatial and temporal predictive coding scheme. During coding with JVET,a frame is first divided into square blocks called CPUs 100, as shown inFIG. 1. For example, CTUs 100 can be blocks of 128×128 pixels.

FIG. 2a depicts an exemplary partitioning of a CTU 100 into CUs 102.Each CTU 100 in a frame can be partitioned into one or more CUs (CodingUnits) 102. CUs 102 can be used for prediction and transform asdescribed below. Unlike HEVC, in JVET the CUs 102 can be rectangular orsquare and can be coded without further partitioning into predictionunits or transform units. The CUs 102 can be as large as their root CTUs100, or be smaller subdivisions of a root CTU 100 as small as 4×4blocks.

In JVET, a CTU 100 can be partitioned into CUs 102 according to aquadtree plus binary tree (QTBT) scheme in which the 100 can berecursively split into square blocks according to a quadtree, and thosesquare blocks can then be recursively split horizontally or verticallyaccording to binary trees. Parameters can be set to control splittingaccording to the QTBT, such as the CTU size, the minimum sizes for thequadtree and binary tree leaf nodes, the maximum size for the binarytree root node, and the maximum depth for the binary trees. In VVC, aCTU 100 can be portioned into CUs utilizing ternary splitting also.

By way of a non-limiting example, FIG. 2a shows a ETU 100 partitionedinto CUs 102, with solid lines indicating quadtree splitting and dashedlines indicating binary tree splitting. As illustrated, the binarysplitting allows horizontal splitting and vertical splitting to definethe structure of the CTU and its subdivision into CUs. FIGS. 2b & 2 cdepict alternate, non-limiting examples of ternary splitting of a CUwherein subdivisions of the CUs are non-equal.

FIG. 3 depicts a QTBT representation of FIG. 2's partitioning. Aquadtree root node represents the CTU 100, with each child node in thequadtree portion representing one of four square blocks split from aparent square block. The square blocks represented by the quadtree leafnodes can then be divided zero or more times using binary trees, withthe quadtree leaf nodes being root nodes of the binary trees. At eachlevel of the binary tree portion, a block can be divided eithervertically or horizontally. A flag set to “0” indicates that the blockis split horizontally, while a flag set to “1” indicates that the blockis split vertically.

After quadtree splitting and binary tree splitting, the blocksrepresented by the QTBT's leaf nodes represent the final CUs 102 to becoded, such as coding using inter prediction or intra prediction. Forslices or full frames coded with inter prediction, differentpartitioning structures can be used for Mina and chroma components. Forexample, for an inter slice a CU 102 can have Coding Blocks (CBs) fordifferent color components, such as such as one luma CB and two chromaCBs. For slices or full frames coded with intra prediction, thepartitioning structure can be the same for luma and chroma components.

FIG. 4 depicts a simplified block diagram for CU coding in a JVETencoder. The main stages of video coding include partitioning toidentify CUs 102 as described above, followed by encoding CUs 102 usingprediction at 404 or 406, generation of a residual CU 410 at 408,transformation at 412, quantization at 416, and entropy coding at 420.The encoder and encoding process illustrated in FIG. 4 also includes adecoding process that is described in more detail below.

Given a current CU 102, the encoder can obtain a prediction CU 402either spatially using intra prediction at 404 or temporally using interprediction at 406. The basic idea of prediction coding is to transmit adifferential, or residual, signal between the original signal and aprediction for the original signal. At the receiver side, the originalsignal can be reconstructed by adding the residual and the prediction,as will be described below. Because the differential signal has a lowercorrelation than the original signal, fewer bits are needed for itstransmission.

A slice, such as an entire picture or a portion of a picture, codedentirely with intra-predicted CUs can be an I slice that can be decodedwithout reference to other slices, and as such can be a possible pointwhere decoding can begin. A slice coded with at least someinter-predicted CUs can be a predictive (P) or bi-predictive (B) slicethat can be decoded based on one or more reference pictures. P slicesmay use intra-prediction and inter-prediction with previously codedslices. For example, P slices may be compressed further than theI-slices by the use of inter-prediction, but need the coding of apreviously coded slice to code them. B slices can use data from previousand/or subsequent slices for its coding, using intra-prediction orinter-prediction using an interpolated prediction from two differentframes, thus increasing the accuracy of the motion estimation process.In some cases P slices and B slices can also or alternately be encodedusing intra block copy, in which data from other portions of the sameslice is used.

As will be discussed below, intra prediction or inter prediction can beperformed based on reconstructed CUs 434 from previously coded CUs 102,such as neighboring, CUs 102 or CUs 102 in reference pictures.

When a CU 102 is coded spatially with intra prediction at 404, an intraprediction mode can be found that best predicts pixel values of the CU102 based on samples from neighboring CUs 102 in the picture.

When coding a CU's luma component, the encoder can generate a list ofcandidate infra prediction modes. While HEVC had 35 possible intraprediction modes for luma components, in JVET there are 67 possibleintra prediction modes for luma components and in VVC there are 85prediction modes. These include a planar mode that uses a threedimensional plane of values generated from neighboring pixels, a DC modethat uses values averaged from neighboring pixels, the 65 directionalmodes shown in FIG. 5 that use values copied from neighboring pixelsalong the solid-line indicated directions and 18 wide-angle predictionmodes that can be used with non-square blocks.

When generating a list of candidate intra prediction modes for a CU'sluma component, the number of candidate modes on the list can depend onthe CU's size. The candidate list can include: a subset of HEVC's 35modes with the lowest SATD (Sum of Absolute Transform Difference) costs;new directional modes added for JVET that neighbor the candidates foundfrom the HEVC modes; and modes from a set of six most probable modes(MPMs) for the CU 102 that are identified based on intra predictionmodes used for previously coded neighboring blocks as well as a list ofdefault modes.

When coding a CU's chroma components, a list of candidate intraprediction modes can also be generated. The list of candidate modes caninclude modes generated with cross-component linear model projectionfrom luma samples, intra prediction modes found for luma CBs inparticular collocated positions in the chroma block, and chromaprediction modes previously found for neighboring blocks. The encodercan find the candidate modes on the lists with the lowest ratedistortion costs, and use those intra prediction modes when coding theCU's luma and chroma components. Syntax can be coded in the bitstreamthat indicates the intra prediction modes used to code each CU 102.

After the best intra prediction modes for a CU 102, have been selected,the encoder can generate a prediction CU 402 using those modes. When theselected modes are directional modes, a 4-tap filter can be used toimprove the directional accuracy. Columns or rows at the top or leftside of the prediction block can be adjusted with boundary predictionfilters, such as 2-tap or 3-tap filters.

The prediction CU 402 can be smoothed further with a position dependentintra prediction combination (PDPC) process that adjusts a prediction CU402 generated based on filtered samples of neighboring blocks usingunfiltered samples of neighboring blocks, or adaptive reference samplesmoothing using 3-tap or 5-tap low pass filters to process referencesamples.

When a CU 102 is coded temporally with inter prediction at 406, a set ofmotion vectors (MVs) can be found that points to samples in referencepictures that best predict pixel values of the CU 102. Inter predictionexploits temporal redundancy between slices by representing adisplacement of a block of pixels in a slice, The displacement isdetermined according to the value of pixels in previous or followingslices through a process called motion compensation. Motion vectors andassociated reference indices that indicate pixel displacement relativeto a particular reference picture can be provided in the bitstream to adecoder, along with the residual between the original pixels and themotion compensated pixels. The decoder can use the residual and signaledmotion vectors and reference indices to reconstruct a block of pixels ina reconstructed slice.

In JVET, motion vector accuracy can be stored at 1/16 pel, and thedifference between a motion vector and a CU's predicted motion vectorcan be coded with either quarter-pel resolution or integer-pelresolution.

In JVET motion vectors can be found for multiple sub-CUs within a CU102, using techniques such as advanced temporal motion vector prediction(ATMVP), spatial-temporal motion vector prediction (STMVP)), affinemotion compensation prediction, pattern matched. motion vectorderivation (PMMVD), and/or bi-directional optical flow (BIO).

Using ATMVP, the encoder can find a temporal vector for the CU 102 thatpoints to a corresponding block in a reference picture. The temporalvector can be found based on motion vectors and reference pictures foundfor previously coded neighboring CUs 102. Using the reference blockpointed to by a temporal vector for the entire CU 102, a motion vectorcan be found for each sub-CU within the CU 102.

STMVP can find motion vectors for sub-CUs by scaling and averagingmotion vectors found for neighboring blocks previously coded with interprediction, together with a temporal vector.

Affine motion compensation prediction can be used to predict a field ofmotion vectors for each sub-CU in a block, based on two control motionvectors found for the top corners of the block. For example, motionvectors for sub CUs can be derived based on top corner motion vectorsfound for each 4×4 block within the CU 102.

PMMVD can find an initial motion vector for the current CU 102 usingbilateral matching or template matching. Bilateral matching can look atthe current CU 102 and reference blocks in two different referencepictures along a motion trajectory, while template matching can look atcorresponding blocks in the current CU 102 and a reference pictureidentified by a template. The initial motion vector found for the CU 102can then be refined individually for each sub-CU.

BIO can be used when inter prediction is performed with bi-predictionbased on earlier and later reference pictures, and allows motion vectorsto be found for sub-CUs based on the gradient of the difference betweenthe two reference pictures.

In some situations local illumination compensation (LIC) can be used atthe CU level to find values for a scaling factor parameter and an offsetparameter, based on samples neighboring the current CU 102 andcorresponding samples neighboring a reference block identified by acandidate motion vector. In JVET, the LIC parameters can change and besignaled at the CU level.

For some of the above methods the motion vectors found for each of aCU's sub-CUs can be signaled to decoders at the CU level. For othermethods, such as PMMVD and BIO, motion information is not signaled inthe bitstream to save overhead, and decoders can derive the motionvectors through the same processes.

After the motion vectors for a CU 102 have been found, the encoder cangenerate a prediction CU 402 using those motion vectors. In some cases,when motion vectors have been found for individual sub-CUs, OverlappedBlock Motion Compensation (OBMC) can be used when generating aprediction CU 402 by combining those motion vectors with motion vectorspreviously found for one or more neighboring sub-CUs.

When bi-prediction is used, JVET can use decoder-side motion vectorrefinement (DMVR) to find motion vectors. DMVR allows a motion vector tobe found based on two motion vectors found for bi-prediction using abilateral template matching process. In DMVR, a weighted combination ofprediction CUs 402 generated with each of the two motion vectors can befound, and the two motion vectors can be refined by replacing them withnew motion vectors that best point, to the combined prediction CU 402.The two refined motion vectors can be used to generate the finalprediction CU 402.

At 408, once a prediction CU 402 has been found with intra prediction at404 or inter prediction at 406 as described above, the encoder cansubtract the prediction CU 402 from the current CU 102 find a residualCU 410.

The encoder can use one or more transform operations at 412 to convertthe residual CU 410 into transform coefficients 414 that express theresidual CU 410 in a transform domain, such as using a discrete cosineblock transform (DCT-transform) to convert data into the transformdomain. JVET allows more types of transform operations than HEVC,including DCT-II, DST-VII, DST-VII, DCT-VIII, DCT-I, and DCT-Voperations, The allowed transform operations can be grouped intosub-sets, and an indication of which sub-sets and which specificoperations in those sub-sets were used can be signaled by the encoder.In some cases, large block-size transforms can be used to zero out highfrequency transform coefficients in CUs 102 larger than a certain size,such that only lower-frequency transform coefficients are maintained forthose CUs 102.

To some cases a mode dependent nonseparable secondary transform (MDNSST)can be applied to low frequency transform coefficients 414 after aforward core transform. The MDNSST operation can use a Hypercube-GivensTransform (HyGT) based on rotation data. When used, an index valueidentifying a particular MDNSST operation can be signaled by theencoder.

At 416, the encoder can quantize the transform coefficients 414 intoquantized transform coefficients 416. The quantization of eachcoefficient may be computed by dividing a value of the coefficient by aquantization step, which is derived from a quantization parameter (QP).in some embodiments, the Qstep is defined as 2^((QP−4)/6). Because highprecision transform coefficients 414 can be converted into quantizedtransform coefficients 416 with a finite number of possible values,quantization can assist with data compression. Thus, quantization of thetransform coefficients may limit an amount of bits generated and sent bythe transformation process. However, while quantization is a lossyoperation, and the loss by quantization cannot be recovered, thequantization process presents a trade-off between quality of thereconstructed sequence and an amount of information needed to representthe sequence. For example, a lower QP value can result in better qualitydecoded video, although a higher amount of data may be required forrepresentation and transmission. In contrast, a high QP value can resultin lower quality reconstructed video sequences but with lower data andbandwidth needs.

JVET can utilize variance-based adaptive quantization techniques, whichallows every CU 102 to use a different quantization parameter for itscoding process (instead of using the same frame QP in the coding ofevery CU 102 of the frame). The variance-based adaptive quantizationtechniques adaptively lowers the quantization parameter of certainblocks while increasing it in others. To select a specific QP for a CU102, the CU's variance is computed. In brief, if a CU's variance ishigher than the average variance of the frame, a higher QP than theframe's QP may be set for the CU 102. If the CU 102 presents a lowervariance than the average variance of the frame, a lower QP may beassigned.

At 420, the encoder can find final compression bits 422 by entropycoding the quantized transform coefficients 418, Entropy coding aims toremove statistical redundancies of the information to be transmitted. InJVET, CABAC (Context Adaptive Binary Arithmetic Coding) can be used tocode the quantized transform coefficients 418, which uses probabilitymeasures to remove the statistical redundancies. For CUs 102 withnon-zero quantized transform coefficients 418, the quantized transformcoefficients 418 can be converted into binary. Each bit (“bin”) of thebinary representation can then be encoded using a context model. A CU102 can be broken up into three regions, each with its own set ofcontext models to use for pixels within that region.

Multiple scan passes can be performed to encode the bins. During passesto encode the first three bins (bin0, bin1, and bin2), an index valuethat indicates which context model to use for the bin can be found byfinding the sum of that bin position in up to five previously codedneighboring quantized transform coefficients 418 identified by atemplate.

A context model can be based on probabilities of a bin's value being ‘0’or ‘1’. As values are coded, the probabilities in the context model canbe updated based on the actual number of ‘0’ and ‘1’ values encountered.While HEVC used fixed tables to re-initialize context models for eachnew picture, in JVET the probabilities of context models for newinter-predicted pictures can be initialized based on context modelsdeveloped for previously coded inter-predicted pictures.

The encoder can produce a bitstream that contains entropy encoded hits422 of residual CUs 410, prediction information such as selected intraprediction modes or motion vectors, indicators of how the CUs 102 werepartitioned from a CTU 100 according to the QTBT structure, and/or otherinformation about the encoded video. The bitstream can be decoded by adecoder as discussed below.

In addition to using the quantized transform coefficients 418 to findthe final compression bits 422, the encoder can also use the quantizedtransform coefficients 418 to generate reconstructed CUs 434 byfollowing the same decoding process that a decoder would use to generatereconstructed CUs 434. Thus, once the transformation coefficients havebeen computed and quantized by the encoder, the quantized transformcoefficients 418 may be transmitted to the decoding loop in the encoder.After quantization of a CU's transform coefficients, a decoding loopallows the encoder to generate a reconstructed CU 434 identical to theone the decoder generates in the decoding process. Accordingly, theencoder can use the same reconstructed CUs 434 that a decoder would usefor neighboring CUs 102 or reference pictures when performing intraprediction or inter prediction for a new CU 102. Reconstructed CUs 102,reconstructed slices, or full reconstructed frames may serve asreferences for further prediction stages.

At the encoder's decoding loop (and see below, for the same operationsin the decoder) to obtain pixel values for the reconstructed image, adequantization process may be performed. To dequantize a frame, forexample, a quantized value for each pixel of a frame is multiplied bythe quantization step, e.g., (Qstep) described above, to obtainreconstructed dequantized transform coefficients 426. For example, inthe decoding process shown in FIG. 4 in the encoder, the quantizedtransform coefficients 418 of a residual CU 410 can be dequantized at424 to find dequantized transform coefficients 426, if an MDNSSToperation was performed during encoding, that operation can be reversedafter quantization.

At 428, the dequantized transform coefficients 426 can be inversetransformed to find a reconstructed residual CU 430, such as by applyinga DCT to the values to obtain the reconstructed image. At 432 thereconstructed residual GU 430 can be added to a corresponding predictionCU 402 found with intra prediction at 404 or inter prediction at 406, inorder to find a reconstructed CU 434.

At 436, one or more filters can be applied to the reconstructed dataduring the decoding process (in the encoder or, as described below, inthe decoder), at either a picture level or CU level, For example, theencoder can apply a deblocking filter, a sample adaptive offset (SAO)filter, and/or an adaptive loop filter (ALF). The encoder's decodingprocess may implement filters to estimate and transmit to a decoder theoptimal filter parameters that can address potential artifacts in thereconstructed image. Such improvements increase the objective andsubjective quality of the reconstructed video. In deblocking filtering,pixels near a sub-CU boundary may be modified, whereas in SAO, pixels ina CTU 100 may be modified using either an edge offset or band offsetclassification. JVET's ALF can use filters with circularly symmetricshapes for each 2×2 block. An indication of the size and identity of thefilter used for each 2×2 block can be signaled.

If reconstructed pictures are reference pictures, they can be stored ina reference buffer 438 for inter prediction of future CUs 102 at 406.

During the above steps, JVET allows a content adaptive clippingoperations to be used to adjust color values to fit between lower andupper clipping bounds. The clipping bounds can change for each slice,and parameters identifying the bounds can be signaled in the bitstream.

FIG. 6 depicts a simplified block diagram for CU coding in a JVETdecoder. A JVET decoder can receive a bitstream containing informationabout encoded CUs 102. The bitstream can indicate how CUs 102 of apicture were partitioned from a CTU 100 according to a QTBT structure,prediction information for the CUs 102 such as intra prediction modes ormotion vectors, and bits 602 representing entropy encoded residual CUs.

At 604 the decoder can decode the entropy encoded bits 602 using theCABAC context models signaled in the bitstream by the encoder. Thedecoder can use parameters signaled by the encoder to update the contextmodels' probabilities in the same way they were updated during encoding.

After reversing the entropy encoding at 604 to find quantized transformcoefficients 606, the decoder can dequantize them at 608 to finddequantized transform coefficients 610. If an MDNSST operation wasperformed during encoding, that operation can be reversed by the decoderafter dequantization.

At 612, the dequantized transform coefficients 610 can be inversetransformed to find a reconstructed residual CU 614. At 616, thereconstructed residual CU 614 can be added to a corresponding predictionCU 626 found with intra prediction at 622 or inter prediction at 624, inorder to find a reconstructed CU 618.

At 620, one or more filters can be applied to the reconstructed data, ateither a picture level or CU level. For example, the decoder can apply adeblocking filter, a sample adaptive offset (SAO) filter, and/or anadaptive loop filter (ALF). As described above, the in-loop filterslocated in the decoding loop of the encoder may be used to estimateoptimal filter parameters to increase the objective and subjectivequality of a frame. These parameters are transmitted to the decoder tofilter the reconstructed frame at 620 to match the filteredreconstructed frame in the encoder, MON After reconstructed pictureshave been generated by finding reconstructed CUs 618 and applyingsignaled filters, the decoder can output the reconstructed pictures asoutput video 628, if reconstructed pictures are to be used as referencepictures, they can be stored in a reference buffer 630 for interprediction of future CUs 102 at 624.

Planar mode is often the most frequently used intra coding mode in VVC,HEW and JVET. FIGS. 7a and 7b show VVC, HEVC and JVET Planar predictorgeneration process for horizontal prediction calculation (FIG. 7a ) 700and vertical predictor calculation (FIG. 7b ) 710 for a coding unit(block) with height H=8 702 and width W=8 704, where the (0,0) 706coordinator corresponds to the top-left position within the coding CU.

Planar mode in VVC, HEVC and JVET (HEVC Planar) generates a first orderapproximation of the prediction for a current Coding Unit (CU), byforming a plane based on the intensity values of the neighboring pixels.Due to the raster-scan coding order, the reconstructed left columnneighboring pixels and the reconstructed top row neighboring pixels areavailable for a current CU, but not the right column neighboring pixelsand the bottom row neighboring pixels, The planar predictor generationprocess of VVC, HEVC and JVET sets the intensity values of all the rightcolumn neighboring pixels to be the same as the intensity value of thetop right neighboring pixel, and the intensity values of all the bottomrow pixels to be the same as the intensity value of the bottom leftneighboring pixel.

Once the neighboring pixels surrounding predicting block are defined,the horizontal and the vertical predictors (P_(h)(x,y) and P_(v)(x,y),respectively) for each pixel within the CU can be determined accordingto following equations:

P _(h)(x,y)=(W−1−x)*R(−1,y)+(x+1)*R(W,−1)

P _(v)(x,y)=(H−1−y)*R(x,−1)+(y+1)*R(−1,H)

wherein

-   -   R(x,y) denotes the intensity value of the reconstructed        neighboring pixel at (x,y) coordinate;    -   W is block width; and    -   H is block height,

From these values, the final planar predictor, P(x,y), is computed byaveraging the horizontal and vertical predictors, with certainadjustment when the current CU is non-square according to the followingequation:

${P\left( {x,y} \right)} = \frac{{H*{P_{h}\left( {x,y} \right)}} + {W*{P_{v}\left( {x,y} \right)}}}{2*H*W}$

This planar prediction concept can be applied to determine MVs at finegranularity, In VVC and JVET, each 4×4 sub-block within a CU can haveits own MV. If MV(x,y) is assumed to be a MV of a sub-block containingpixel (x,y) and the planar infra prediction concept described earlierthen, a reconstructed pixel, R(x,y), can be converted to a MV at thesub-block level, MV(x,y), noting specifically that for intra planar,R(x,y) is a 1D intensity, while for inter planar, P(x,y) is a 2-D MV.That is, the horizontal and the vertical predictors P_(h)(x,y) andP_(v)(x,y) respectively can be converted to the horizontal and thevertical MV predictors (MV_(h)(x,y) and MV_(v)(x,y), respectively. Thenthe final Planar MV, MV(x,y) can be computed by averaging the horizontaland vertical predictors.

In some embodiments, multiple reference slices can be available fortemporal prediction. Hence, neighboring sub-blocks can use differencereferences for their associated MVs. For simplicity, one reference canbe used when combining more than one MV where more than one reference isemployed. in some embodiment, one possible option to combine multiplereferences is to select the reference of the neighbor sub-block closest,in terms of POC distance, to the coding picture.

Planar derivation of MB can require the MV of the CU surroundingneighbor sub-blocks. However, in some embodiments, it may be possiblethat some neighboring sub-blocks can be considered unavailable forplanar derivation, because they may not have a suitable MV. By way ofnon-limiting example, the neighboring sub-block can be coded with intramode, may not use appropriate reference list, or may not use anappropriate reference slice. In such cases, a default MV or a substituteMV can be used in place of the specific MV of the neighboring sub-block.In such a situation, a substitute MV can be used, for example, based onthe MV of the first available adjacent neighbors, in alternateembodiments in which a neighboring sub-block MV doesn't use appropriatereference slice in presented, one possible option is to scale theavailable MV to the desired reference slice with weighting factor(s)according to a ratio of temporal distances.

The present disclosure presents a system and method to derive the MV ofthe bottom right neighboring sub-block for a current CU (liftingprocess), and then compute the MV of the bottom row and the right columnneighboring sub-blocks, using the derived MV of the bottom rightneighboring sub-block along with the MV of other corner neighboring subblocks, such as the top right neighboring sub-block, the bottom leftneighboring sub-block.

In some embodiments, the bottom right sub-block MV derivation processcan be a weighted average of the top right and the bottom leftneighboring sub-blocks, as defined in on the equations presented below:

${{{MV}\left( {W,H} \right)} = \frac{{W*{{MV}\left( {W,{- 1}} \right)}} + {H*{{MV}\left( {{- 1},H} \right)}}}{H + W}}{{{MV}\left( {W,H} \right)} = \frac{{H*{{MV}\left( {W,{- 1}} \right)}} + {W*{{MV}\left( {{- 1},H} \right)}}}{H + W}}$

In alternate embodiments, a flat plane can be assumed and based on theMV of the top left, the top right, and the bottom left neighboringsub-blocks, the MV can be derived based on the following equation:

MV(W,H)=MV(W,−1)+MV(−1,H)−MV(−1,−1)

where position (0, 0) denotes the top-left sub-block position of thecurrent block, W is the width of the current block and H is the heightof the current block, MV(x, y) can therefore denote the MV of thereconstructed sub-block containing pixel at position (x, y) as well asthe estimated/predicted MV at sub-block containing position (x, y).

Yet another non-limiting example can be based on deriving the bottomright sub-block MV using the MV of the sub-block at co-located positioncontaining pixel (W,H) in the co-located reference (similar to TMVPderivation).

Having derived the MV of the bottom right neighboring sub-block ofMV(W,H), the MV of the bottom row neighboring sub-blocks, MV_(b)(x, H),and the right column neighboring sub-blocks, MV_(r)(W,y), can becomputed. if linear interpolation is assumed then the MVs can be definedas follows:

${{{MV}_{b}\left( {x,H} \right)} = \frac{{\left( {W - 1 - x} \right)*{{MV}\left( {{- 1},H} \right)}} + {\left( {x + 1} \right)*{{MV}\left( {W,H} \right)}}}{W}}{{{MV}_{r}\left( {W,y} \right)} = \frac{{\left( {H - 1 - y} \right)*{{MV}\left( {W,{- 1}} \right)}} + {\left( {y + 1} \right)*{{MV}\left( {W,H} \right)}}}{H}}$

However, in alternate embodiments, models other than linearinterpolation can be used to the relevant

Once the motion vectors of neighboring sub-blocks surrounding a currentCU are defined, the horizontal and the vertical MV (MV_(h)(x, y) andMV_(v)(x, v), respectively) for each sub-block within the CU can bedetermined according to the following equations:

MV _(h)(x,y)=(W−1−x)*MV(−1,y)+(x+1)*MV _(r)(W,y)

MV _(v)(x,y)≤(H−1−y)*MV(x,−1)+(y+1)*MV _(b)(x,H)

Wherein MV_(h)(x,y) and MV_(v)(x,y) are scaled up versions of horizontaland vertical MV predictors, However, these factors can be compensatedfor in the final MV predictor calculation step.

In some embodiments, top-right and bottom-left corner sub-blockpositions can be set to be MV(W−1,−1) and MV(−1,H−1), respectively. Insuch embodiments, interpolation for intermediate predictors can bedescribed as an example by of the following equations:

${{{MV}\left( {{W - 1},{H - 1}} \right)} = \frac{{W*{{MV}\left( {{W - 1},{- 1}} \right)}} + {H*{{MV}\left( {{- 1},{H - 1}} \right)}}}{H + W}}{{{MV}_{b}\left( {x,{H - 1}} \right)} = \frac{\begin{matrix}{{\left( {W - 1 - x} \right)*{MV}\left( {{- 1},{H - 1}} \right)} +} \\{\left( {x + 1} \right)*{{MV}\left( {{W - 1},{H - 1}} \right)}}\end{matrix}}{W}}{{{MV}_{r}\left( {{W - 1},y} \right)} = \frac{\begin{matrix}{{\left( {H - 1 - y} \right)*{MV}\left( {{W - 1},{- 1}} \right)} +} \\{\left( {y + 1} \right)*{{MV}\left( {{W - 1},{H - 1}} \right)}}\end{matrix}}{H}}{{{MV}_{h}\left( {x,y} \right)} = {{\left( {W - 1 - x} \right)*{{MV}\left( {{- 1},y} \right)}} + {\left( {x + 1} \right)*{{MV}_{r}\left( {{W - 1},y} \right)}}}}{{{MV}_{v}\left( {x,y} \right)} = {{\left( {H - 1 - y} \right)*{{MV}\left( {x,{- 1}} \right)}} + {\left( {y + 1} \right)*{{MV}_{b}\left( {x,{H - 1}} \right)}}}}$

Some embodiments can utilize an unequal-weight combination for the finalPlanar MV derivation. Unequal weight can be employed to take advantageof differences in accuracy of input intensity in the final interpolationprocess. Specifically, larger weights can be applied to sub-blockpositions that are closer to more reliable neighboring sub-blockpositions. In VVC and JVET, the processing order follows raster scan atthe CTU level and r-scan for CUs within a CTU. Hence, the top row andthe left column neighboring sub-blocks are the actual reconstructedsub-blocks and are more reliable than the bottom row and right columnneighboring sub-blocks, which are estimated, An example applicationusing unequal weight employed at the final MV derivation is described inthe following equation:

${{MV}\left( {x,y} \right)} = \frac{{H*{{MV}_{h}\left( {x,y} \right)}*\left( {y + 1} \right)} + {W*{{MV}_{v}\left( {x,y} \right)}*\left( {x + 1} \right)}}{H*W*\left( {x + y + 2} \right)}$

And an example of unequal weight assignment shown in the above equationcan be generalized into a generic equation as shown below:

${{MV}\left( {x,y} \right)} = \frac{{{A\left( {x,y} \right)}*{{MV}_{h}\left( {x,y} \right)}} + {{B\left( {x,y} \right)}*{{MV}_{v}\left( {x,y} \right)}} + {c\left( {x,y} \right)}}{D\left( {x,y} \right)}$

where A(x,y) and B(x,y) are the position dependent weighting factors forhorizontal and vertical predictors, respectively, c(x,y) is a positiondependent rounding factor, and D(x,y) is a position dependent scalingfactor.

It should be noted that unequal weight assignment can also be used atthe horizontal and vertical MV predictor computation phase and that thebottom right position adjustment and unequal weight assignmentcomponents can be used together or separately depending on codec designconsiderations. In some embodiments, weighting factors and liftingprocess can be modified according to picture type (I, P, B, and/or anyother known, convenient and/or desired type), temporal layer, colorcomponent (Y, Cb, Cr, and/or any other known convenient and/or desiredcolor component).

In some embodiments, a special merge mode with a unique merge candidateindex can be used to signal usage of unequal weight planar MVderivation. In embodiments in which this special merge mode is selectedthe merge sub-block MV can be computed according to the following thefollowing equations:

${{{MV}\left( {x,y} \right)} = \frac{{H*{{MV}_{h}\left( {x,y} \right)}*\left( {y + 1} \right)} + {W*{{MV}_{v}\left( {x,y} \right)}*\left( {x + 1} \right)}}{H*W*\left( {x + y + 2} \right)}}{{{MV}\left( {x,y} \right)} = \frac{{{A\left( {x,y} \right)}*{{MV}_{h}\left( {x,y} \right)}} + {{B\left( {x,y} \right)}*{{MV}_{v}\left( {x,y} \right)}} + {c\left( {x,y} \right)}}{D\left( {x,y} \right)}}$

Equations associated with the bottom right sub-block position adjustmentprocess and unequal weight assignment process, respectively, aspresented above involve division operations, which can be costly interms of computational complexity. However, these division operationscan be generally be converted into scale operations to make them moreefficient and implementation friendly, as presented in the belowequations:

MV(W,H)=((W*MV(W,−1)+H*MV(−1,H))*S[W+H])»ShiftDenom

MV(W,H)=((H*MV(W,−1)+W*MV(−1,H))*S[W+H])»ShiftDenom

MV _(b)(x,H)=(((W−1−x)*MV(−1,H)+(x+1)*MV(W,H))*S[W])»ShiftDenom

MV _(r)(W,y)=(((H−1−y)*MV(W,−1)+(y+1)*MV(W,H))*S[H])»ShiftDenom

MV(x,y)=((H*MV _(h)(x,y)*(y+1)+W*MV_(v)(x,y)*(x+1))*S[x+y+2])»(ShiftDenom+log₂ W+log ₂ H)

where S[n] is a weight factor of parameter n, and ShiftDenom is a factorfor shifted down operation. Specifically, S[n] is an approximation ofthe factor 1/n, and can be described as:

${S\lbrack n\rbrack} = {{Round}\left( \frac{1{\operatorname{<<}{ShiftDenom}}}{n} \right)}$

An example of S[n]800, where sum of width and height is 256 andShiftDenom is 10 is depicted in FIG. 8 and another example of S[n] 900,where sum of width and height is 512 and ShiftDenom is 10 is depicted inFIG. 9.

In the examples presented in FIGS. 8 and 9, a memory size of 2570 bits(257 entries with 10 bits each for FIG. 8) and 5130 bits (513 entrieswith 10 bits each for FIG. 9) are required to hold the weight tables.This memory size may be excessive and memory burdensome and thus it canbe beneficial to efficiency and for memory management to reduce thismemory requirement. Below are presented two examples of two possibleways to accomplish reduced size of S[n] and memory burden.

Presented below is a non-limiting example of S[n], where sum of widthand height is 128 and ShiftDenom is 10 is shown below.

-   -   S[n]={341, 256, 205, 171, 146, 128, 114, 102, 93 85, 79, 73, 68,        64, 66, 57, 54, 51, 49, 47, 45, 43, 41, 39, 38, 37, 35, 34, 33,        32, 31, 30, 29, 28, 28, 27, 26, 26, 25, 24, 24, 23, 23, 22, 22,        21, 21, 20, 20, 20, 19, 19, 19, 18, 18, 18, 17, 17, 17, 17, 16        16, 16, 16 15, 15, 15, 15, 14, 14, 14, 14, 14, 13, 13, 13, 13,        13, 13, 12, 12, 12, 12, 12, 12, 12, 12, 11, 11, 21, 11, 11, 11,        11, 11, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 9, 9, 9, 9, 9,        9, 9, 9, 9, 9, 9, 9, 9, 8, 8, 8, 8, 8, 8, 8, 8}

Another non-limiting example of of S[n], where sum of width and heightis 128 and ShiftDenom is 9 is shown below.

S[n]={171, 128, 102, 85, 73, 54, 57, 51, 47, 43, 39, 37, 34, 32, 30, 2827, 26, 24, 23, 22, 21, 26, 20, 19, 18, 18, 17, 17, 16 16, 15, 15, 14,14, 13, 13, 13, 12, 12, 12, 12, 11 11, 11, 11, 10, 10, 10, 10, 10, 9, 9,9, 9, 9, 9, 9, 8, 8, 8, 8, 8, 8, 8, 8, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 6,6, 6, 6, 6, 5, 6, 6, 6, 6, 6, 5, 6, 6, 6, 5, 5, 5, 5, 5, 5, 5, 5, 5, 55, 5, 5, 5, 5, 5, 5, 5, 5, 5, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,4}

In the above examples, only 126 entries of 129 necessary are required tobe stored, since the first two entries (1/0 and 1/1) are not used in thesystem and method presented. Additionally, the third entry, representing½, has value of 512 and 256 in the above examples, and it can be handledseparately during weight calculation. Accordingly, weight averagecalculations shown in the above-presented equations:

MV _(b)(x,H)=(((W−1−x)*MV(−1,H)+(x+1)*MV(W,H))*S[W])»ShiftDenom

MV _(r)(W,y)=(((H−1−y)*MV(W,−1)+(y+1)*MV(W,H))*S[H])»ShiftDenom

can be modified as shown in the below equations:

MV _(b)(x,H)=(((W−1−x)*MV(−1,H)+(x+1)*MV(W,H))*S[W−3])»ShiftDenom

MV _(r)(W,y)=(((H−1−y)*MV(W,−1)+(y+1)*MV(W,H))*S[H−3])»ShiftDenom

However, simple shift conversion as identified by

${S\lbrack n\rbrack} = {{Round}\left( \frac{1{\operatorname{<<}{ShiftDenom}}}{n} \right)}$

may not provide accurate outputs, thus resulting in poor codingefficiency. The ineffectiveness can be due to the conversion processwhich can allow errors to accumulate linearly with distance. In someembodiments, this error can be reduced by exploiting the fact that theweight for horizontal and vertical predictors are complimentary in thefollowing equation:

${{MV}\left( {x,y} \right)} = \frac{{H*{{MV}_{h}\left( {x,y} \right)}*\left( {y + 1} \right)} + {W*{{MV}_{v}\left( {x,y} \right)}*\left( {x + 1} \right)}}{H*W*\left( {x + y + 2} \right)}$

Accordingly, weighting can be computed based on that of the horizontalor vertical predictor, whichever is more accurate. This can beaccomplished by introducing parameters horWeight and verWeight areintroduced into the following equation:

${S\lbrack n\rbrack} = {{Round}\left( \frac{1{\operatorname{<<}{ShiftDenom}}}{n} \right)}$

which results in following equations:

${{MV}\left( {x,y} \right)} = {\left( {{H*{{MV}_{h}\left( {x,y} \right)}*{horWeight}} + {W*{{MV}_{v}\left( {x,y} \right)}*{verWeight}}} \right) \gg {{{\left( {{ShiftDenom} + {\log_{2}W} + {\log_{2}H}} \right){{hor}{Weight}}} = \left\{ {{\begin{matrix}{\left( {1 \ll {ShiftDenom}} \right) - {verWeight}} & {{{when}x} < y} \\{\left( {y + 1} \right)*{S\left\lbrack {x + y + 2} \right\rbrack}} & {otherwise}\end{matrix}{{ver}{Weight}}} = \left\{ \begin{matrix}{\left( {x + 1} \right)*{S\left\lbrack {x + y + 2} \right\rbrack}} & {{{when}x} < y} \\{\left( {1 \ll {ShiftDenom}} \right) - {horWeight}} & {otherwise}\end{matrix} \right.} \right.}}}$

Alternately, the following equations can be employed when reduced tablesas identified above are used.

${horWeight} = \left\{ {{\begin{matrix}{\left( {1 \ll {ShiftDenom}} \right) - {verWeight}} & {{{when}x} < y} \\{\left( {y + 1} \right)*{S\left\lbrack {x + y - 1} \right\rbrack}} & {otherwise}\end{matrix}{verWeight}} = \left\{ {{{\begin{matrix}{\left( {x + 1} \right)*{S\left\lbrack {x + y - 1} \right\rbrack}} & {{{when}x} < y} \\{\left( {1 \ll {ShiftDenom}} \right) - {horWeight}} & {otherwise}\end{matrix}{horWeight}} = {{verWeifht} = \left( {1 \ll \left( {{ShiftDenom} - 1} \right)} \right)}},{{{when}x} = {y = 0}}} \right.} \right.$

In some embodiments, the table size to store weight of parameter, S[n],can be further reduced since unequal weight planar MV derivationoperates at the sub-block level, since the sub-block level canconfigured at a coarser granularity, such as 4×4 in VVC and JVET, ratherthan at the pixel level.

Thus, when a sub-block. size is N×N, then MV(x,y) can be mapped intosub-block coordinate MV(x/N,y/N). With a reduced dimension from W×H to(W/N)×(H/N), the maximum size of table is correspondingly lower, thus asmaller table size can be used. Accordingly, the above equations can bereformulated and presented as:

$\left. {{{MV}\left( {\frac{x}{N},\frac{y}{N}} \right)} = {{{\left( {{\frac{H}{N}*{{MV}_{h}\left( {\frac{x}{N},\frac{y}{N}} \right)}*{horWeight}} + {\frac{W}{N}*{{MV}_{v}\left( {\frac{x}{N},\frac{y}{N}} \right)}*{verWeight}}} \right) \gg {{{ShiftDenom} + {\log_{2}\left( \frac{W}{N} \right)} + {\log_{2}\left( \frac{H}{N} \right)}}}}}}} \right){{horWeight} = \left\{ {{\begin{matrix}{\left( {1 \ll {ShiftDenom}} \right) - {verWeight}} & {{{when}\frac{x}{N}} < \frac{y}{N}} \\{\left( {\frac{y}{N} + 1} \right)*{S\left\lbrack {\frac{x}{N} + \frac{y}{N} - 1} \right\rbrack}} & {otherwise}\end{matrix}{verWeight}} = \left\{ \begin{matrix}{\left( {\frac{x}{N} + 1} \right)*{S\left\lbrack {\frac{x}{N} + \frac{y}{N} - 1} \right\rbrack}} & {{{when}\frac{x}{N}} < \frac{y}{N}} \\{\left( {1 \ll {ShiftDenom}} \right) - {horWeight}} & {otherwise}\end{matrix} \right.} \right.}$

FIG. 10 depicts a block flow diagram 1000 of a system and method ofunequal weight planar motion vector derivation. In step 1002 a CU isreceived then in step 1004 motion information associated with the CU isdetermined. Then in step 1006 motion information associated with thebottom right neighboring pixel or block is derived based on motioninformation associated with the CU. Then in steps 1008 and 1010 motioninformation can be derived and/or defined based at least in part on themotion information associated with the CU and motion informationassociated with the derived bottom right neighboring pixel or block inaccordance with the systems and methods described herein. While FIG. 10depicts step 1008 preceding step

In some embodiments, steps 1008 and 1010 can proceed in parallel and/orstep 1010 can preceded step 1008. In step 1012 it is determined whetherunequal weighting techniques were used to derive the relevant motionvectors. If in step 1012 it is determined that weighting was notemployed, then the system can proceed to encoding, as described herein,in step 1014. If however in step 1012 it is determined that motioninformation was derived using a weighted combination technique, then anindicator can be set in step 1016 and the system can proceed to encodingin step 1014.

The execution of the sequences of instructions required to practice theembodiments can be performed by a computer system 1100 as shown in FIG.11. In an embodiment, execution of the sequences of instructions isperformed by a single computer system 1100. According to otherembodiments, two or more computer systems 1100 coupled by acommunication link 1115 can perform the sequence of instructions incoordination with one another. Although a description of only onecomputer system 1100 will be presented below, however, it should beunderstood that any number of computer systems 1100 can be employed topractice the embodiments.

A computer system 1100 according to an embodiment will now be describedwith reference to FIG. 11, which is a block diagram of the functionalcomponents of a computer system 1100. As used herein, the term computersystem 1100 is broadly used to describe any computing device that canstore and independently run one or more programs.

Each computer system 1100 can include a communication interface 1114coupled to the bus 1106. The communication interface 1114 providestwo-way communication between computer systems 1100. The communicationinterface 1114 of a respective computer system 1100 transmits andreceives electrical, electromagnetic or optical signals, that includedata streams representing various types of signal information, e.g.,instructions, messages and data. A communication link 1115 links onecomputer system 1100 with another computer system 1100, For example, thecommunication link 1115 can be a LAN, in which case the communicationinterface 1114 can be a LAN card, or the communication link 1115 can bea PSTN, in which case the communication interface 1114 can be anintegrated services digital network (ISDN) card or a modem, or thecommunication link 1115 can be the Internet, in which case thecommunication interface 1114 can be a dial-up, cable or wireless modern.

A computer system 1100 can transmit and receive messages, data, andinstructions, including program, i.e., application, code, through itsrespective communication link 1115 and communication interface 1114.Received program code can be executed by the respective processor(s)1107 as it is received, and/or stored in the storage device 1110, orother associated non-volatile media, for later execution.

In an embodiment, the computer system 1100 operates in conjunction witha data storage system 1131, e.g., a data storage system 1131 thatcontains a database 1132 that is readily accessible by the computersystem 1100. The computer system 1100 communicates with the data storagesystem 1131 through a data interface 1133. A data interface 1133, whichis coupled to the bus 1106, transmits and receives electrical,electromagnetic or optical signals, that include data streamsrepresenting various types of signal information, e.g., instructions,messages and data. In embodiments, the functions of the data interface1133 can be performed by the communication interface 1114.

Computer system 1100 includes a bus 1106 or other communicationmechanism for communicating instructions, messages and data,collectively, information, and one or more processors 1107 coupled withthe bus 1106 for processing information. Computer system 1100 alsoincludes a main memory 1108, such as a random access memory (RAM) orother dynamic storage device, coupled to the bus 1106 for storingdynamic data and instructions to be executed by the processor(s) 1107.The main memory 1108 also can be used for storing temporary data, i.e.,variables, or other intermediate information during execution ofinstructions by the processor(s) 1107.

The computer system 1100 can further include a read only memory (ROM)1109 or other static storage device coupled to the bus 1106 for storing,static data and instructions for the processor(s) 1107. A storage device1110, such as a magnetic disk or optical disk, can also be provided andcoupled to the bus 1106 for storing data and instructions for theprocessor(s) 1107.

A computer system 1100 can be coupled via the bus 1106 to a displaydevice 1111, such as, but not limited to, a cathode ray tube (CRT) or aliquid-crystal display (LCD) monitor, for displaying information to auser. An input device 1112, e.g., alphanumeric and other keys, iscoupled to the bus 1106 for communicating information and commandselections to the processor(s) 1107.

According to one embodiment, an individual computer system 1100 performsspecific operations by their respective processor(s) 1107 executing oneor more sequences of one or more instructions contained in the mainmemory 1108. Such instructions can be read into the main memory 1108from another computer-usable medium, such as the ROM 1109 or the storagedevice 1110. Execution of the sequences of instructions contained in themain memory 1108 causes the processor(s) 1107 to perform the processesdescribed herein. In alternative embodiments, hard-wired circuitry canbe used in place of or in combination with software instructions. Thus,embodiments are not limited to any specific combination of hardwarecircuitry and/or software.

The term “computer-usable medium,” as used herein, refers to any mediumthat provides information or is usable by the processor(s) 1107. Such amedium can take many forms, including, but not limited to, non-volatile,volatile and transmission media. Non-volatile media, i.e., media thatcan retain information in the absence of power, includes the ROM 1109,CD ROM, magnetic tape, and magnetic discs, Volatile media, i.e., mediathat can not retain information in the absence of power, includes themain memory 1108. Transmission media includes coaxial cables, copperwire and fiber optics, including the wires that comprise the bus 1106.Transmission media can also take the form of carrier waves; i.e.,electromagnetic waves that can be modulated, as in frequency, amplitudeor phase, to transmit information signals, Additionally, transmissionmedia can take the form of acoustic or light waves, such as thosegenerated during radio wave and infrared data communications.

In the foregoing specification, the embodiments have been described withreference to specific elements thereof. It will, however, be evidentthat various modifications and changes can be made thereto withoutdeparting from the broader spirit and scope of the embodiments. Forexample, the reader is to understand that the specific ordering andcombination of process actions shown in the process flow diagramsdescribed herein is merely illustrative, and that using different oradditional process actions, or a different combination or ordering ofprocess actions can be used to enact the embodiments. The specificationand drawings are, accordingly, to be regarded in an illustrative ratherthan restrictive sense.

It should also be noted that the present invention can be implemented ina variety of computer systems. The various techniques described hereincan be implemented in hardware or software, or a combination of both.Preferably, the techniques are implemented in computer programsexecuting on programmable computers that each include a processor, astorage medium readable by the processor (including volatile andnon-volatile memory and/or storage element, at least one input device,and at least one output device. Program code is applied to data enteredusing the input device to perform the functions described above and togenerate output information. The output information is applied to one ormore output devices. Each program is preferably implemented in a highlevel procedural or object oriented programming language to communicatewith a computer system. However, the programs can be implemented inassembly or machine language, if desired. In any case, the language canbe a compiled or interpreted language. Each such computer program ispreferably stored on a storage medium or device (e.g., ROM or magneticdisk) that is readable by a general or special purpose programmablecomputer for configuring and operating the computer when the storagemedium or device is read by the computer to perform the proceduresdescribed above. The system can also be considered to be implemented asa computer-readable storage medium, configured with a computer program,where the storage medium so configured causes a computer to operate in aspecific and predefined manner. Further, the storage elements of theexemplary computing applications can be relational or sequential (flatfile) type computing databases that are capable of storing data invarious combinations and configurations.

FIG. 12 is a high level view of a source device 1212 and destinationdevice 1210 that may incorporate features of the systems and devicesdescribed herein. As shown in FIG. 12, example video coding system 1210includes a source device 1212 and a destination device 1214 where, inthis example, the source device 1212 generates encoded video data.Accordingly, source device 1212 may be referred to as a video encodingdevice. Destination device 1214 may decode the encoded video datagenerated by source device 1212. Accordingly, destination device 1214may be referred to as a video decoding device. Source device 1212 anddestination device 1214 may be examples of video coding devices.

Destination device 1214 may receive encoded video data from sourcedevice 1212 via a channel 1216. Channel 1216 may comprise a type ofmedium or device capable of moving the encoded video data from sourcedevice 1212 to destination device 1214. In one example, channel 1216 maycomprise a communication medium that enables source device 1212 totransmit encoded. video data directly to destination device 1214 inreal-time.

in this example, source device 1212 may modulate the encoded video dataaccording to a communication standard, such as a wireless communicationprotocol, and may transmit the modulated video data to destinationdevice 1214. The communication medium may comprise a wireless or wiredcommunication medium, such as a radio frequency (RF) spectrum or one ormore physical transmission lines. The communication medium may form partof a packet-based network, such as a local area network, a wide-areanetwork, or a global network such as the Internet. The communicationmedium may include routers, switches, base stations, or other equipment,that facilitates communication from source device 1212 to destinationdevice 1214. In another example, channel 1216 may correspond to astorage medium that stores the encoded video data generated by sourcedevice 1212.

In the example of FIG. 12, source device 1212 includes a video source1218, video encoder 1220, and an output interface 1222. In some cases,output interface 1228 may include a modulator/demodulator (modem) and/ora transmitter. In source device 1212, video source 1218 may include asource such as a video capture device, e.g., a video camera, a videoarchive containing previously captured video data, a video feedinterface to receive video data from a video content provider, and/or acomputer graphics system for generating video data, or a combination ofsuch sources.

Video encoder 1220 may encode the captured, pre-captured, orcomputer-generated video data. An input image may be received by thevideo encoder 1220 and stored in the input frame memory 1221. Thegeneral purpose processor 1223 may load information from here andperform encoding. The program for driving the general purpose processormay be loaded from a storage device, such as the example memory modulesdepicted in FIG. 12. The general purpose processor may use processingmemory 1222 to perform the encoding, and the output of the encodinginformation by the general processor may be stored in a buffer, such asoutput buffer 1226.

The video encoder 1220 may include a resampling module 1225 which may beconfigured to code (e.g., encode) video data in a scalable video codingscheme that defines at least one base layer and at least one enhancementlayer. Resampling module 1225 may resample at least some video data aspart of an encoding process, wherein resampling may be performed in anadaptive manner using resampling filters.

The encoded video data, e.g., a coded bit stream, may be transmitteddirectly to destination device 1214 via output interface 1228 of sourcedevice 1212. In the example of FIG. 12, destination device 1214 includesan input interface 1238, a video decoder 1230, and a display device1232. In some cases, input interface 1228 may include a receiver and/ora modern, Input interface 1238 of destination device 1214 receivesencoded video data over channel 1216. The encoded video data may includea variety of syntax elements generated by video encoder 1220 thatrepresent the video data. Such syntax elements may be included with theencoded video data transmitted on a communication medium, stored on astorage medium, or stored a file server.

The encoded video data may also be stored onto a storage medium or atile server for later access by destination device 1214 for decodingand/or playback. For example, the coded bitstrearn may be temporarilystored in the input buffer 1231, then loaded in to the general purposeprocessor 1233. The program for driving the general purpose processormay be loaded from a storage device or memory. The general purposeprocessor may use a process memory 1232 to perform the decoding. Thevideo decoder 1230 may also include a resampling module 1235 similar tothe resampling module 1225 employed in the video encoder 1220.

FIG. 12 depicts the resampling module 1235 separately from the generalpurpose processor 1233, but it would be appreciated by one of skill inthe art that the resampling function may be performed by a programexecuted by the general purpose processor, and the processing in thevideo encoder may be accomplished using one or more processors. Thedecoded image(s) may be stored in the output frame buffer 1236 and thensent out to the input interface 1238.

Display device 1238 may be integrated with or may be external todestination device 1214. In some examples, destination device 1214 mayinclude an integrated display device and may also be configured tointerface with an external display device. In other examples,destination device 1214 may be a display device. In general, displaydevice 1238 displays the decoded video data to a user.

Video encoder 1220 and video decoder 1230 may operate according to avideo compression standard. ITU-T VCEG (Q6/16) and ISO/IEC MPEG (JTC1/SC 29/WG 11) are studying the potential need for standardization offuture video coding technology with a compression capability thatsignificantly exceeds that of the current High Efficiency Video CodingHEVC standard (including its current extensions and near-term extensionsfor screen content coding and high-dynamic-range coding). The groups areworking together on this exploration activity in a joint collaborationeffort known as the Joint Video Exploration Team (JVET) to evaluatecompression technology designs proposed by their experts in this area. Arecent capture of JVET development is described in the “AlgorithmDescription of Joint Exploration Test Model 5 (JEM 5)” JVET-E1001-V2,authored by J. Chen, E. Alshina, G. Sullivan, J. Ohm, J. Boyce.

Additionally or alternatively, video encoder 1220 and video decoder 1230may operate according to other proprietary or industry standards thatfunction with the disclosed JVET features. Thus, other standards such asthe ITU-T H.264 standard, alternatively referred to as MPEG-4, Part 10,Advanced Video Coding (AVC), or extensions of such standards. Thus,while newly developed for JVET, techniques of this disclosure are notlimited to any particular coding standard or technique. Other examplesof video compression standards and techniques include MPEG-2, T H.263and proprietary or open source compression formats and related formats.

Video encoder 1220 and video decoder 1230 may be implemented inhardware, software, firmware or any combination thereof. For example,the video encoder 1220 and decoder 1230 may employ one or moreprocessors, digital signal processors (DSPs), application specificintegrated circuits (ASICs), field programmable gate arrays (FPGAs),discrete logic, or any combinations thereof. When the video encoder 1220and decoder 1230 are implemented partially in software, a device maystore instructions for the software in a suitable, non-transitorycomputer-readable storage medium and may execute the instructions inhardware using one or more processors to perform the techniques of thisdisclosure. Each of video encoder 1220 and video decoder 1230 may beincluded in one or more encoders or decoders, either of which may beintegrated as part of a combined encoder/decoder (CODEC) in a respectivedevice.

Aspects of the subject matter described herein may be described in thegeneral context of computer-executable instructions, such as programmodules, being executed by a computer, such as the general-purposeprocessors 1223 and 1233 described above. Generally, program modulesinclude routines, programs, objects, components, data structures, and soforth, which perform particular tasks or implement particular abstractdata types. Aspects of the subject matter described herein may also bepracticed in distributed computing environments where tasks areperformed by remote processing devices that are linked through acommunications network. In a distributed computing environment, programmodules may be located in both local and remote computer storage mediaincluding memory storage devices.

Examples of memory include random access memory (RA), read only memory(ROM) or both, Memory may store instructions, such as source code orbinary code, for performing the techniques described above. Memory mayalso be used for storing variables or other intermediate informationduring execution of instructions to be executed by a processor, such asprocessor 1223 and 1233.

A storage device may also store instructions, instructions, such assource code or binary code, for performing the techniques describedabove. A storage device may additionally store data used and manipulatedby the computer processor, For example, a storage device in a videoencoder 1220 or a video decoder 1230 may be a database that is accessedby computer system 1223 or 1233. Other examples of storage deviceinclude random access memory (RAM), read only memory (ROM), a harddrive, a magnetic disk, an optical disk, a CD-ROM, a DVD, a flashmemory, a USB memory card, or any other medium from which a computer canread.

A memory or storage device may be an example of a non-transitorycomputer-readable storage medium for use by or in connection with thevideo encoder and/or decoder. The non-transitory computer-readablestorage medium contains instructions for controlling a computer systemto be configured to perform functions described by particularembodiments. The instructions, when executed by one or more computerprocessors, may be configured to perform that which is described inparticular embodiments.

Also, it is noted that some embodiments have been described as a processwhich can be depicted as a flow diagram or block diagram. Although eachmay describe the operations as a sequential process, many of theoperations can be performed in parallel or concurrently. In addition,the order of the operations may be rearranged. A process may haveadditional steps not included in the figures.

Particular embodiments may be implemented in a non-transitorycomputer-readable storage medium for use by or in connection with theinstruction execution system, apparatus, system, or machine. Thecomputer-readable storage medium contains instructions for controlling acomputer system to perform a method described by particular embodiments.The computer system may include one or more computing devices. Theinstructions, when executed by one or more computer processors, may beconfigured to perform that which is described in particular embodiments.

As used in the description herein and throughout the claims that follow,“a”, “an”, and “the” includes plural references unless the contextclearly dictates otherwise, Also, as used in the description herein andthroughout the claims that follow, the meaning of “in” includes “in” and“on” unless the context clearly dictates otherwise.

Although exemplary embodiments of the invention have been described indetail and in language specific to structural features and/ormethodological acts above, it is to be understood that those skilled inthe art will readily appreciate that many additional modifications arepossible in the exemplary embodiments without materially departing fromthe novel teachings and advantages of the invention. Moreover, it is tobe understood that the subject matter defined in the appended claims isnot necessarily to the specific features or acts described above.

Accordingly, these and all such modifications are intended to beincluded within the scope of this invention construed in breadth andscope in accordance with the appended claims.

What is claimed is:
 1. A method of decoding a video included in abitstream by a decoder, comprising: (a) receiving said bitstreamindicating how a coding tree unit was partitioned into coding unitsaccording to a quad tree plus multi tree structure that allows a squareparent node to be split with a quaternary tree partitioning that splitssaid square parent node in half in both horizontal and verticaldirections to define leaf nodes that are square in shape each of whichare the same size, wherein said quad tree plus multi tree structureallows one of said leaf nodes to be split based upon one selected from agroup consisting of, (i) a symmetric binary tree partitioning thatsplits one of said leaf nodes of said quaternary tree partitioning inhalf in either a horizontal direction or a vertical direction resultingin two blocks that are the same size as leaf nodes, and (ii) anasymmetric tree partitioning that splits one of said leaf nodes of saidquaternary tree partitioning in either a horizontal direction or avertical direction resulting in three blocks two of which are differentin size than a third one of said three blocks as leaf nodes; (b)identifying final coding units to be decoded represented by leaf nodesof the quad tree plus multi tree structure; (c) receiving both (1) afirst motion vector associated with a coding unit of a B-slice of acurrent frame of said video, where one of said final coding units issaid coding unit included in a bi-predictive slice of said current frameof said video, referencing a temporally previous reference slice of atemporally previous reference frame relative to said current frame ofsaid coding unit, and a (2) a second motion vector associated with saidcoding unit of said B-slice of said current frame of said videoreferencing a temporally future reference slice of a temporally futureframe relative to said current frame of said coding unit; (d) whereinsaid coding unit of said B-slice of said current frame of said video hasa top neighboring row, a left neighboring column, a bottom neighboringrow and a right neighboring column, each of said top neighboring row,left neighboring column, bottom neighboring row, and right neighboringcolumn in the said current frame; (e) modifying at least one of saidfirst motion vector and said second motion vector based at least in partupon the other of said at least one of said first motion vector and saidsecond motion vector and based upon an optical flow of said temporallyprevious reference slice and said temporally future reference slice; (f)decoding said coding unit based upon said modified at least one of saidfirst motion together with said temporally previous reference slice ofsaid temporally previous frame and said second motion vector togetherwith said temporally future reference slice of said temporally futureframe.